import cv2
from matplotlib import pyplot as plt
import numpy as np
import os
import scipy.signal as sgn
from scipy.optimize import minimize
from matplotlib.patches import Rectangle
from mpl_toolkits import mplot3d
# %matplotlib inline
%matplotlib ipympl
%matplotlib ipympl

from scipy.optimize import curve_fit

2d and 3d Plots of Axis Lengths, Their Differences, and Angles

save_arr_dir = 'C:/Users/Kazim/Desktop/Overall/VignettingCircleDetection/savedarrays/091123_ellipse_cv2_fit_tiff_size/'
fit_range = np.loadtxt(f'{save_arr_dir}fit_range.txt').reshape([7, 7])
center_cords = np.loadtxt(f'{save_arr_dir}center.txt').reshape([7, 7, 2])
axis_lens = np.loadtxt(f'{save_arr_dir}axis_lens.txt').reshape([7, 7, 2])
angles_saved = np.loadtxt(f'{save_arr_dir}angles_all.txt').reshape([7, 7])

center_cords_extended = np.loadtxt('extrap_280923/center_cords_extended.txt').reshape(11, 11, 2)
axis_lens_extended = np.loadtxt('extrap_280923/axis_lens_entended.txt').reshape(11, 11, 2)
angles_extended = np.loadtxt('extrap_280923/angles_extended.txt').reshape(11, 11)

center_cords[0, 0] = np.nan, np.nan
center_cords[0, 6] = np.nan, np.nan
center_cords[-1, 0] = np.nan, np.nan
center_cords[-1, 6] = np.nan, np.nan

axis_lens[0, 0] = np.nan, np.nan
axis_lens[0, 6] = np.nan, np.nan
axis_lens[-1, 0] = np.nan, np.nan
axis_lens[-1, 6] = np.nan, np.nan

angles_saved[0, 0] = np.nan
angles_saved[0, -1] = np.nan
angles_saved[-1, 0] = np.nan
angles_saved[-1, -1] = np.nan


axis_lens_extended[2:9, 2:9, 0] = axis_lens[:, :, 1]
axis_lens_extended[2:9, 2:9, 1] = axis_lens[:, :, 0]
center_cords_extended[2:9, 2:9] = center_cords
angles_extended[2:9, 2:9] = angles_saved

axis_lens_extended[1, :2] = [0.0, 0.0]
axis_lens_extended[9, 8:10] = [0.0, 0.0]
axis_lens_extended[9, 8:10] = [0.0, 0.0]
axis_lens_extended[8:10, 9] = [0.0, 0.0]
axis_lens_extended[1, 1] = [0.0, 0.0]
axis_lens_extended[:4, 0] = [0.0, 0.0]
axis_lens_extended[7:, 0] = [0.0, 0.0]

angles_extended[0, :4] = np.nan; angles_extended[0, 7:] = np.nan
angles_extended[1, :2] = np.nan; angles_extended[1, 9:] = np.nan
angles_extended[2, 0] = np.nan; angles_extended[2, -1] = np.nan
angles_extended[3, 0] = np.nan; angles_extended[3, -1] = np.nan
angles_extended[4, -1] = np.nan
angles_extended[6, -1] = np.nan
angles_extended[7, 0] = np.nan; angles_extended[7, -1] = np.nan
angles_extended[8, 0] = np.nan; angles_extended[8, -2:] = np.nan
angles_extended[9, :2] = np.nan; angles_extended[9, -3:] = np.nan
angles_extended[10, :] = np.nan

axis_lens_extended[np.where(axis_lens_extended==0.)] = np.nan

fig, ax = plt.subplots(1, 2, figsize=(15, 8), layout='tight')

xx, yy = np.meshgrid([i for i in range(10, 21)], [i for i in range(10, 21)])

cp = ax[0].imshow(np.array([list(reversed(axis_lens_extended[i, :])) for i in range(axis_lens_extended.shape[0])])[:, :, 1].T, interpolation='none',
                  extent=[10, 21, 10, 21]
                 )

ax[0].grid()
ax[0].scatter(xx+0.5, yy+0.5, marker=".", color="black")
ax[0].set_title("Minor Axis Lengths")
ax[0].set_xlabel('Y LED Positions')
ax[0].set_ylabel('X LED Positions')

someX, someY = 12, 12
ax[0].add_patch(Rectangle((someX, someY), 7, 7, facecolor="none", ec='red', lw=2))

fig.colorbar(cp)

cp = ax[1].imshow(np.array([list(reversed(axis_lens_extended[i, :])) for i in range(axis_lens_extended.shape[0])])[:, :, 0].T, interpolation='none', 
                 extent=[10, 21, 10, 21]
                 )

ax[1].grid()
ax[1].scatter(xx+0.5, yy+0.5, marker=".", color="black")
ax[1].set_title("Major Axis Lengths")
ax[1].set_xlabel('Y LED Positions')
ax[1].set_ylabel('X LED Positions')

someX, someY = 12, 12
ax[1].add_patch(Rectangle((someX, someY), 7, 7, facecolor="none", ec='red', lw=2))

fig.colorbar(cp)

plt.show()

Figure

plt.figure()
xx, yy = np.meshgrid([i for i in range(10, 21)], [i for i in range(10, 21)])

diff_axis = axis_lens_extended[:, :, 0] - axis_lens_extended[:, :, 1]

cp = plt.imshow(np.array([list(reversed(diff_axis[i, :])) for i in range(diff_axis.shape[0])]).T, 
                interpolation='none', extent=[10, 21, 10, 21]
                 )
# fig.colorbar(cp)
plt.grid()
plt.scatter(xx+0.5, yy+0.5, marker=".", color="black")
plt.title("Difference bw Min Maj Axis")
plt.xlabel('Y LED Positions')
plt.ylabel('X LED Positions')
# plt.tight_layout()

someX, someY = 12, 12
currentAxis = plt.gca()
currentAxis.add_patch(Rectangle((someX, someY), 7, 7, facecolor="none", ec='red', lw=2))

fig.colorbar(cp)
plt.show()

Figure

plt.figure()
xx, yy = np.meshgrid([i for i in range(10, 21)], [i for i in range(10, 21)])
cp = plt.imshow(np.array([list(reversed(angles_extended[i, :])) for i in range(angles_extended.shape[0])]).T,
                 interpolation='none', extent=[10, 21, 10, 21], 
                 cmap='twilight'
                 )
# fig.colorbar(cp)
plt.grid()
plt.scatter(xx+0.5, yy+0.5, marker=".", color="black")
plt.title("Angles")
plt.xlabel('X LED Positions')
plt.ylabel('Y LED Positions')
# plt.tight_layout()

someX, someY = 12, 12
currentAxis = plt.gca()
currentAxis.add_patch(Rectangle((someX, someY), 7, 7, facecolor="none", ec='red', lw=2))

fig.colorbar(cp)
plt.show()

Figure

xx, yy = np.meshgrid([i for i in range(10, 21)], [i for i in range(10, 21)])

fig = plt.figure()
ax = plt.axes(projection='3d')

ax.plot_surface(yy, xx, axis_lens_extended[:, :, 0], rstride=1, cstride=1,
                cmap='viridis', edgecolor='none')
ax.set_title('Major Axis Lengths');
ax.set_xlabel('X Axis')
ax.set_ylabel('Y Axis');
ax.view_init(90, -91)

Figure

fig = plt.figure()
ax = plt.axes(projection='3d')

ax.plot_surface(yy, xx, axis_lens_extended[:, :, 1], rstride=1, cstride=1,
                cmap='viridis', edgecolor='none')
ax.set_title('Minor Axis Lengths');
ax.set_xlabel('X Axis')
ax.set_ylabel('Y Axis');
ax.view_init(90, -91)

Figure

fig = plt.figure()
ax = plt.axes(projection='3d')

ax.plot_surface(yy, xx, axis_lens_extended[:, :, 0] - axis_lens_extended[:, :, 1], rstride=1, cstride=1,
                cmap='viridis', edgecolor='none')
ax.set_title('Diff MajMin Axis Lengths');
ax.set_xlabel('X Axis')
ax.set_ylabel('Y Axis');
ax.view_init(90, -91)

Figure

fig = plt.figure()
ax = plt.axes(projection='3d')
ax.plot_surface(yy, xx, angles_extended, rstride=1, cstride=1,
                cmap='twilight', edgecolor='none')
ax.set_title('Angles');
ax.set_xlabel('X Axis')
ax.set_ylabel('Y Axis');
ax.view_init(90, -91)

Figure

FITTING

z_ax0 = np.delete(axis_lens[:, :, 0].reshape(-1, 1), [0, 6, -7, -1], 0)
z_ax1 = np.delete(axis_lens[:, :, 1].reshape(-1, 1), [0, 6, -7, -1], 0)
z_angles = np.delete(angles_saved[:, :].reshape(-1, 1), [0, 6, -7, -1], 0)
y1, x1 = np.meshgrid([i for i in range(12, 19)], [i for i in range(12, 19)])
x1 = np.delete(x1[:, :].reshape(-1, 1), [0, 6, -7, -1], 0)
y1 = np.delete(y1[:, :].reshape(-1, 1), [0, 6, -7, -1], 0)

from sklearn.linear_model import Ridge, Lasso
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import RepeatedKFold

# additional features with degree 2
x2 = x1**2
y2 = y1**2
xy = x1*y1
x3 = x1**3
y3 = y1**3

X = np.array([x1, y1, x2, y2, xy, x3, y3]).squeeze().T

# define model
model_ax0 = Ridge(alpha=0.5, solver='lsqr')
# define model evaluation method
cv = RepeatedKFold(n_splits=10, n_repeats=3, random_state=1)
# evaluate model
scores = cross_val_score(model_ax0, X, z_ax0, scoring='neg_mean_absolute_error', cv=cv, n_jobs=-1)
# force scores to be positive
scores = np.absolute(scores)
print('Mean MAE: %.3f (%.3f)' % (np.mean(scores), np.std(scores)))

model_ax0.fit(X, z_ax0)

w_z_ax0 = model_ax0.coef_
print(w_z_ax0)

Mean MAE: 14.149 (4.644)
[[ 0.18209488  0.03107323  2.88452691 -0.03847153 -0.02654681 -0.12702866
  -0.02022648]]

# define model
model_ax1 = Ridge(alpha=0.5, solver='lsqr')
# define model evaluation method
cv = RepeatedKFold(n_splits=10, n_repeats=3, random_state=1)
# evaluate model
scores = cross_val_score(model_ax1, X[:, :-2], z_ax1, scoring='neg_mean_absolute_error', cv=cv, n_jobs=-1)
# force scores to be positive
scores = np.absolute(scores)
print('Mean MAE: %.3f (%.3f)' % (np.mean(scores), np.std(scores)))

model_ax1.fit(X[:, :-2], z_ax1)

w_z_ax1 = model_ax1.coef_
print(w_z_ax1)

Mean MAE: 6.209 (4.404)
[[-6.83235681 -4.38549775  0.02671118  0.02110941  0.28043923]]

y1, x1 = np.meshgrid([i for i in range(10, 21)], [i for i in range(10, 21)])
x1 = x1.reshape(-1, 1)
y1 = y1.reshape(-1, 1)
x2 = x1**2
y2 = y1**2
xy = x1*y1
x3 = x1**3
y3 = y1**3
predX = np.array([x1, y1, x2, y2, xy, x3, y3]).squeeze().T

est_z_ax0 = model_ax0.predict(predX)
est_z_ax0 = est_z_ax0.reshape(11, 11)

est_z_ax1 = model_ax1.predict(predX[:, :-2])
est_z_ax1 = est_z_ax1.reshape(11, 11)

fig, ax = plt.subplots(1, 2, figsize=(12, 7), layout='tight')

xx, yy = np.meshgrid([i for i in range(10, 21)], [i for i in range(10, 21)])

cp = ax[0].imshow(np.array([list(reversed(est_z_ax0[i, :])) for i in range(est_z_ax0.shape[0])]).T, interpolation='none',
                  extent=[10, 21, 10, 21]
                 )

ax[0].grid()
ax[0].scatter(xx+0.5, yy+0.5, marker=".", color="black")
ax[0].set_title("Minor Axis Lengths")
ax[0].set_xlabel('Y LED Positions')
ax[0].set_ylabel('X LED Positions')

someX, someY = 12, 12
ax[0].add_patch(Rectangle((someX, someY), 7, 7, facecolor="none", ec='red', lw=2))

fig.colorbar(cp)

cp = ax[1].imshow(np.array([list(reversed(est_z_ax1[i, :])) for i in range(est_z_ax1.shape[0])]).T, interpolation='none',
                  extent=[10, 21, 10, 21]
                 )

ax[1].grid()
ax[1].scatter(xx+0.5, yy+0.5, marker=".", color="black")
ax[1].set_title("Major Axis Lengths")
ax[1].set_xlabel('Y LED Positions')
ax[1].set_ylabel('X LED Positions')

someX, someY = 12, 12
ax[1].add_patch(Rectangle((someX, someY), 7, 7, facecolor="none", ec='red', lw=2))

fig.colorbar(cp);

Figure

fig = plt.figure(figsize=plt.figaspect(0.5))

ax = fig.add_subplot(1, 2, 1, projection='3d')

ax.plot_surface(yy, xx, est_z_ax0, rstride=1, cstride=1,
                cmap='viridis', edgecolor='none')
ax.set_title('Minor Axis Lengths');
ax.set_xlabel('X Axis')
ax.set_ylabel('Y Axis');
ax.view_init(90, -91)

ax = fig.add_subplot(1, 2, 2, projection='3d')
ax.plot_surface(yy, xx, est_z_ax1, rstride=1, cstride=1,
                cmap='viridis', edgecolor='none')
ax.set_title('Major Axis Lengths');
ax.set_xlabel('X Axis')
ax.set_ylabel('Y Axis');
ax.view_init(90, -91)

Figure

y1, x1 = np.meshgrid([i for i in range(12, 19)], [i for i in range(12, 19)])

angles_saved_copy = angles_saved.copy()
angles_saved_copy[np.where(diff_axis[2:9, 2:9] <= 15)] = np.nan

x1 = np.delete(x1.reshape(-1), np.argwhere(np.isnan(angles_saved_copy.reshape(-1))).reshape(-1))
y1 = np.delete(y1.reshape(-1), np.argwhere(np.isnan(angles_saved_copy.reshape(-1))).reshape(-1))
angles_saved_copy = np.delete(angles_saved_copy.reshape(-1), np.argwhere(np.isnan(angles_saved_copy.reshape(-1))).reshape(-1))
x1, y1, angles_saved_copy

(array([12, 12, 12, 12, 12, 13, 13, 13, 13, 14, 14, 14, 14, 15, 15, 15, 15,
        16, 16, 16, 17, 17, 17, 17, 18, 18, 18, 18, 18]),
 array([13, 14, 15, 16, 17, 15, 16, 17, 18, 15, 16, 17, 18, 15, 16, 17, 18,
        16, 17, 18, 15, 16, 17, 18, 13, 14, 15, 16, 17]),
 array([ 53.,  37.,  43.,  50.,  57.,  58.,  63.,  69.,  71.,  70.,  75.,
         75.,  77.,  88.,  85.,  87.,  87.,  97.,  96.,  96., 116., 113.,
        108., 104., 125., 131., 126., 122., 116.]))

# additional features with degree 2
x2 = x1**2
y2 = y1**2
xy = x1*y1
x3 = x1**3
y3 = y1**3

X = np.array([x1, y1, x2, y2, xy, x3, y3]).squeeze().T

# define model
model_ang = Ridge(alpha=0.5, solver='lsqr')
# define model evaluation method
cv = RepeatedKFold(n_splits=10, n_repeats=3, random_state=1)
# evaluate model
scores = cross_val_score(model_ang, X[:, :-2], angles_saved_copy, scoring='neg_mean_absolute_error', cv=cv, n_jobs=-1)
# force scores to be positive
scores = np.absolute(scores)
print('Mean MAE: %.3f (%.3f)' % (np.mean(scores), np.std(scores)))

model_ang.fit(X[:, :-2], angles_saved_copy)

w_z_ang = model_ang.coef_
print(w_z_ax0)

Mean MAE: 3.634 (1.762)
[[ 0.18209488  0.03107323  2.88452691 -0.03847153 -0.02654681 -0.12702866
  -0.02022648]]

est_z_ang = model_ang.predict(predX[:, :-2])
est_z_ang = est_z_ang.reshape(11, 11)

fig = plt.figure()

xx, yy = np.meshgrid([i for i in range(10, 21)], [i for i in range(10, 21)])

cp = plt.imshow(np.array([list(reversed(est_z_ang[i, :])) for i in range(est_z_ang.shape[0])]).T, interpolation='none',
                  extent=[10, 21, 10, 21], cmap='twilight'
                 )

plt.grid()
plt.scatter(xx+0.5, yy+0.5, marker=".", color="black")
plt.title("Angles")
plt.xlabel('Y LED Positions')
plt.ylabel('X LED Positions')

someX, someY = 12, 12
ax = plt.gca()
ax.add_patch(Rectangle((someX, someY), 7, 7, facecolor="none", ec='red', lw=2))

fig.colorbar(cp)

<matplotlib.colorbar.Colorbar at 0x26d84178f70>

Figure

fig = plt.figure(figsize=(6, 6))
ax = plt.axes(projection='3d')

ax.plot_surface(yy, xx, est_z_ang, rstride=1, cstride=1,
                cmap='twilight', edgecolor='none')
ax.set_title('Angles');
ax.set_xlabel('X Axis')
ax.set_ylabel('Y Axis');
ax.view_init(90, -91)

Figure

## save into manual and extrap suffix txts

# np.savetxt('extrap_280923/axis_lens_entended_manual.txt', axis_lens_extended.reshape(-1, 2))
# np.savetxt('extrap_280923/angles_extended_manual.txt', angles_extended.reshape(-1))
# np.savetxt('extrap_280923/axis_lens_entended_extrap.txt', np.array([est_z_ax0, est_z_ax1]).T.reshape(-1, 2))
# np.savetxt('extrap_280923/angles_extended_extrap.txt', est_z_ang.reshape(-1))

np.array([est_z_ax0, est_z_ax1]).T.shape

(11, 11, 2)

Use Exrapolated Centers, Axis Lengths, and Angles to Plot ELlipse on the Image Grid

%matplotlib inline
%matplotlib inline

plt.figure()
extrap_centers = np.loadtxt('extrap_280923/center_cords_extended_extrap.txt').reshape(11, 11, 2)
extrap_axis_lens = np.loadtxt('extrap_280923/axis_lens_entended_extrap.txt').reshape(11, 11, 2)
extrap_angles = np.loadtxt('extrap_280923/angles_extended_extrap.txt').reshape(11, 11)

extrap_centers_0 = np.array([list(reversed(extrap_centers[i, :])) for i in range(extrap_centers.shape[0])])[:, :, 0].T
extrap_centers_1 = np.array([list(reversed(extrap_centers[i, :])) for i in range(extrap_centers.shape[0])])[:, :, 1].T
extrap_axis_lens_0 = np.array([list(reversed(extrap_axis_lens[i, :])) for i in range(extrap_axis_lens.shape[0])])[:, :, 0].T
extrap_axis_lens_1 = np.array([list(reversed(extrap_axis_lens[i, :])) for i in range(extrap_axis_lens.shape[0])])[:, :, 1].T
extrap_angles = np.array([list(reversed(extrap_angles[i, :])) for i in range(extrap_angles.shape[0])]).T

fig, ax = plt.subplots()

q = ax.quiver(extrap_centers_0, extrap_centers_1, 
              extrap_axis_lens_0*np.cos(-extrap_angles/180), extrap_axis_lens_0*np.sin(-extrap_angles/180), 
              pivot='mid', width=0.003)
ax.quiverkey(q, X=0.3, Y=1.1, U=10,
             label='Quiver key, length = 10', labelpos='E')

r = ax.quiver(extrap_centers_0, extrap_centers_1, 
              extrap_axis_lens_1*np.cos(-extrap_angles/180 + np.pi/2), extrap_axis_lens_1*np.sin(-extrap_angles/180 + np.pi/2), 
              pivot='mid', width=0.003)
ax.quiverkey(r, X=0.3, Y=1.1, U=10,
             label='Quiver key, length = 10', labelpos='E')


ax.plot(extrap_centers_0, extrap_centers_1, 'r.')

ax.grid(which='major', color='#DDDDDD', linewidth=0.8)
ax.grid(which='minor', color='#EEEEEE', linestyle=':', linewidth=0.8)
ax.minorticks_on()

<Figure size 640x480 with 0 Axes>

extrap_centers[:, :, 1].shape, np.zeros([11, 11]).shape, extrap_axis_lens[:, :, 0].shape

((11, 11), (11, 11), (11, 11))

def draw_cross(img, cord, color, length=50, thickness=10):
    
    x, y = int(cord[0]), int(cord[1])
    cv2.line(img, (x-length, y-length), (x+length, y+length), color, thickness)
    cv2.line(img, (x+length, y-length), (x-length, y+length), color, thickness)
    
    return img

extrap_centers = np.loadtxt('extrap_280923/center_cords_extended_extrap.txt').reshape(11, 11, 2)
extrap_axis_lens = np.loadtxt('extrap_280923/axis_lens_entended_extrap.txt').reshape(11, 11, 2)
extrap_angles = np.loadtxt('extrap_280923/angles_extended_extrap.txt').reshape(11, 11)

for x in range(10, 21):
    for y in range(10, 21):

        dir_path = "Z:/CSE\CSE-Research/Microscopy3D/CV_CSE_Collaboration/Results/CV_CSE/fpm_capture/output/2023_03_15/2023_03_15_16_02_07/"
        file_path = f"2023_03_15_16_02_07_img_shutter_05_x_{x}_y_{y}_r_0_g_1_b_0.tiff"
        print('x:', x, 'y:', y, file_path)

        img = cv2.imread(dir_path + file_path, cv2.IMREAD_UNCHANGED)
        img_8 = cv2.convertScaleAbs(img, alpha=(255/65535))
        
        x_cnt, y_cnt = extrap_centers[x-10, y-10]
        len_0, len_1 = extrap_axis_lens[x-10, y-10]

        draw_cross(img_8, (int(x_cnt), int(y_cnt)), (255, 0, 0))
        cv2.ellipse(img_8, (int(x_cnt), int(y_cnt)), (int(len_0), int(len_1)), int(extrap_angles[x-10, y-10]), 0, 360, (255, 0, 0), 10)

        print(f'x:{x}, y:{y}, Center:{x_cnt, y_cnt}, Axis:{len_0, len_1}, Angle:{extrap_angles[x-10, y-10]}')

        plt.imshow(img_8)
        plt.show()

        # break
    # break

x: 10 y: 10 2023_03_15_16_02_07_img_shutter_05_x_10_y_10_r_0_g_1_b_0.tiff
x:10, y:10, Center:(1388.5839632299887, 2558.633798983109), Axis:(2636.4680409575767, 2686.197877787367), Angle:21.043473013842274

x: 10 y: 11 2023_03_15_16_02_07_img_shutter_05_x_10_y_11_r_0_g_1_b_0.tiff
x:10, y:11, Center:(1494.482661439307, 2218.2381539222356), Axis:(2654.9132474697617, 2682.730848011993), Angle:21.96378314589346

x: 10 y: 12 2023_03_15_16_02_07_img_shutter_05_x_10_y_12_r_0_g_1_b_0.tiff
x:10, y:12, Center:(1572.1352181830994, 1927.8628089859549), Axis:(2670.743616467628, 2679.3172405899977), Angle:23.55276599098997

x: 10 y: 13 2023_03_15_16_02_07_img_shutter_05_x_10_y_13_r_0_g_1_b_0.tiff
x:10, y:13, Center:(1624.3765800054628, 1673.0371105366182), Axis:(2683.196976012325, 2675.95705552138), Angle:25.810421549131817

x: 10 y: 14 2023_03_15_16_02_07_img_shutter_05_x_10_y_14_r_0_g_1_b_0.tiff
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